Change detection of natural lake boundaries is one of the important tasks in remote sensing image interpretation. In an ordinary fully connected network, or CNN, the signal of neurons in each layer can only be propagated to the upper layer, and the processing of samples is independent at each moment. However, for time-series data with transferability, the learned change information needs to be recorded and utilized. To solve the above problems, we propose a lake boundary change prediction model combining U-Net and LSTM. The ensemble of LSTMs helps to improve the overall accuracy and robustness of the model by capturing the spatial and temporal nuances in the data, resulting in more precise predictions. This study selected Lake Urmia as the research area and used the annual panoramic remote sensing images from 1996 to 2014 (Lat: 37°00′ N to 38°15′ N, Lon: 46°10′ E to 44°50′ E) obtained by Google Earth Professional Edition 7.3 software as the research data set. This model uses the U-Net network to extract multi-level change features and analyze the change trend of lake boundaries. The LSTM module is introduced after U-Net to optimize the predictive model using historical data storage and forgetting as well as current input data. This method enables the model to automatically fit the trend of time series data and mine the deep information of lake boundary changes. Through experimental verification, the model’s prediction accuracy for lake boundary changes after training can reach 89.43%. Comparative experiments with the existing U-Net-STN model show that the U-Net-LSTM model used in this study has higher prediction accuracy and lower mean square error.

Detecting changes in land cover is a critical task in remote sensing image interpretation, with particular significance placed on accurately determining the boundaries of lakes. Lake boundaries are closely tied to land resources, and any alterations can have substantial implications for the surrounding environment and ecosystem. This paper introduces an innovative end-to-end model that combines U-Net and spatial transformation network (STN) to predict changes in lake boundaries and investigate the evolution of the Lake Urmia boundary. The proposed approach involves pre-processing annual panoramic remote sensing images of Lake Urmia, obtained from 1996 to 2014 through Google Earth Pro Version 7.3 software, using image segmentation and grayscale filling techniques. The results of the experiments demonstrate the model’s ability to accurately forecast the evolution of lake boundaries in remote sensing images. Additionally, the model exhibits a high degree of adaptability, effectively learning and adjusting to changing patterns over time. The study also evaluates the influence of varying time series lengths on prediction accuracy and reveals that longer time series provide a larger number of samples, resulting in more precise predictions. The maximum achieved accuracy reaches 89.3%. The findings and methodologies presented in this study offer valuable insights into the utilization of deep learning techniques for investigating and managing lake boundary changes, thereby contributing to the effective management and conservation of this significant ecosystem.

Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The system identifies droughts and other climate anomalies such as extreme precipitation and heat stress. However, the LIGHTS model lacks input from soil moisture observations. This research aims to develop a simple and easy-to-interpret soil moisture and drought warning index—standardized soil moisture index (SSI)—by fusing the space-borne Soil Moisture Active Passive (SMAP) soil moisture data with the North American Land Data Assimilation System (NLDAS) Noah land surface model (LSM) output. Ground truth soil moisture data from the Soil Climate Analysis Network (SCAN) were collected for validation. As a result, the accuracy of using SMAP to monitor soil moisture content generally displayed a good statistical correlation with the SCAN data. The validation through the Palmer drought severity index (PDSI) and normalized difference water index (NDWI) suggested that SSI was effective and sensitive for short-term drought monitoring across large areas.

The identification of small land targets in remote sensing imagery has emerged as a significant research objective. Despite significant advancements in object detection strategies based on deep learning for visible remote sensing images, the performance of detecting a small and densely distributed number of small targets remains suboptimal. To address this issue, this study introduces an improved model named YOLOV4_CPSBi, based on the YOLOV4 architecture, specifically designed to enhance the detection capability of small land targets in remote sensing imagery. The proposed model enhances the traditional CSPNet by redefining its channel partitioning and integrating this enhanced structure into the neck part of the YOLO network model. Additionally, the conventional pyramid fusion structure used in the traditional BiFPN is removed. By integrating a weight-based bidirectional multi-scale mechanism for feature fusion, the model is capable of effectively reasoning about objects of various sizes, with a particular focus on detecting small land targets, without introducing a significant increase in computational costs. Using the DOTA dataset as research data, this study quantifies the object detection performance of the proposed model. Compared with various baseline models, for the detection of small targets, its AP performance has been improved by nearly 8% compared with YOLOV4. By combining these modifications, the proposed model demonstrates promising results in identifying small land targets in visible remote sensing images.

As a kind of air pollution, haze has complex temporal and spatial characteristics. From the perspective of time, haze has different causes and levels of pollution in different seasons. From the perspective of space, the concentration of haze in adjacent areas will affect each other, showing some correlation. In this paper, we construct a multi-convolution haze-level prediction model for predicting haze levels in different areas of Beijing, which uses the remote sensing satellite image of the Beijing divided into nine regions as input and the haze pollution level as output. We categorize the predictions into four seasons in chronological order and use frequency histograms to analyze haze levels in different regions in different seasons. The results show that the haze pollution in the southern regions is significantly different from that in the northern regions. In addition, the haze tends to be clustered in adjacent areas. We use Global Moran’s I to analyze the predictions and find that haze is related to the geographical location in summer and autumn. We also use Local Moran’s I, Moran scatter plot, and Local Indicators of Spatial Association (LISA) to study the spatial characteristics of haze in adjacent areas. The results show, for the spatial distribution of haze in Beijing, that the southern regions present a high-high agglomeration, while the northern regions exhibit a ‘low-low agglomeration. The temporal evolution of haze on the seasonal scale, according to the chronological order of winter, spring, and summer to autumn, shows that the haze gradually becomes agglomerated. The main finding is that the haze pollution in southern Beijing is significantly different from that of northern regions, and haze tends to be clustered in adjacent areas.